Pathologic myopia (PM) is a common blinding retinal degeneration suffered by
highly myopic population. Early screening of this condition can reduce the
damage caused by the associated fundus lesions and therefore prevent vision
loss. Automated diagnostic tools based on artificial intelligence methods can
benefit this process by aiding clinicians to identify disease signs or to
screen mass populations using color fundus photographs as inputs. This paper
provides insights about PALM, our open fundus imaging dataset for pathological
myopia recognition and anatomical structure annotation. Our databases comprises
1200 images with associated labels for the pathologic myopia category and
manual annotations of the optic disc, the position of the fovea and
delineations of lesions such as patchy retinal atrophy (including peripapillary
atrophy) and retinal detachment. In addition, this paper elaborates on other
details such as the labeling process used to construct the database, the
quality and characteristics of the samples and provides other relevant usage
notes.

该研究提供了有关用于识别 pathologic myopia 的开放式眼底图像数据集 PALM 的信息，包括与其相关的结构注释，为该病的早期筛查以及基于人工智能的自动化诊断工具的开发提供了可能。

PALM：开放式黄斑图像数据集，包含病理性近视识别和解剖结构标注

PALM: Open Fundus Photograph Dataset with Pathologic Myopia Recognition  and Anatomical Structure Annotation

In Fluorescein Angiography (FA), an exogenous dye is injected in the
bloodstream to image the vascular structure of the retina. The injected dye can
cause adverse reactions such as nausea, vomiting, anaphylactic shock, and even
death. In contrast, color fundus imaging is a non-invasive technique used for
photographing the retina but does not have sufficient fidelity for capturing
its vascular structure. The only non-invasive method for capturing retinal
vasculature is optical coherence tomography-angiography (OCTA). However, OCTA
equipment is quite expensive, and stable imaging is limited to small areas on
the retina. In this paper, we propose a novel conditional generative
adversarial network (GAN) capable of simultaneously synthesizing FA images from
fundus photographs while predicting retinal degeneration. The proposed system
has the benefit of addressing the problem of imaging retinal vasculature in a
non-invasive manner as well as predicting the existence of retinal
abnormalities. We use a semi-supervised approach to train our GAN using
multiple weighted losses on different modalities of data. Our experiments
validate that the proposed architecture exceeds recent state-of-the-art
generative networks for fundus-to-angiography synthesis. Moreover, our vision
transformer-based discriminators generalize quite well on out-of-distribution
data sets for retinal disease prediction.

本文提出了一种新的条件生成对抗网络（GAN），能够同时从眼底照片中合成荧光素血管造影图像，并预测视网膜退化，从而解决了以非侵 asive 的方式对眼底血管成像以及预测视网膜异常的问题。